This book, "Models for Discrete Longitudinal Data," authored by Geert Molenberghs and Geert Verbeke, is part of the Springer Series in Statistics. It focuses on models for analyzing discrete longitudinal data, extending the linear mixed model to non-Gaussian settings. The authors, both experts in the field, provide a comprehensive guide to various model families and inferential paradigms, including marginal, conditional, and subject-specific models. The book covers a wide range of topics, such as generalized linear models, generalized estimating equations, pseudo-likelihood, and the generalized linear mixed model (GLMM). It also includes practical examples and case studies, such as the Analgesic Trial, the Fluvoxamine Trial, and the Epilepsy Data, to illustrate the application of these models. The book is designed for a broad audience, including applied statisticians and biomedical researchers, and emphasizes practical aspects over mathematical rigor. It is supported by extensive use of SAS software for model fitting and analysis. The authors acknowledge the contributions of numerous colleagues and research grants that have contributed to the development of the book.This book, "Models for Discrete Longitudinal Data," authored by Geert Molenberghs and Geert Verbeke, is part of the Springer Series in Statistics. It focuses on models for analyzing discrete longitudinal data, extending the linear mixed model to non-Gaussian settings. The authors, both experts in the field, provide a comprehensive guide to various model families and inferential paradigms, including marginal, conditional, and subject-specific models. The book covers a wide range of topics, such as generalized linear models, generalized estimating equations, pseudo-likelihood, and the generalized linear mixed model (GLMM). It also includes practical examples and case studies, such as the Analgesic Trial, the Fluvoxamine Trial, and the Epilepsy Data, to illustrate the application of these models. The book is designed for a broad audience, including applied statisticians and biomedical researchers, and emphasizes practical aspects over mathematical rigor. It is supported by extensive use of SAS software for model fitting and analysis. The authors acknowledge the contributions of numerous colleagues and research grants that have contributed to the development of the book.